Passive Fault-Tolerant Control of a Fixed-Wing UAV Based on Model-Free Control

2021 ◽  
Author(s):  
Ali Srour ◽  
Hassan Noura ◽  
Didier Theilliol
Keyword(s):  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Model Free ◽  
Model Free Control

scholarly journals Active Fault-Tolerant Control Based on Multiple Input Multiple Output-Model Free Adaptive Control for Four Wheel Independently Driven Electric Vehicle Drive System

2019 ◽  
Vol 9 (2) ◽  
pp. 276 ◽  
Author(s):  
Yugong Luo ◽  
Yun Hu ◽  
Fachao Jiang ◽  
Rui Chen ◽  
Yongsheng Wang
Keyword(s):  
Control System ◽  
Active Fault ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Drive System ◽  
Input Output ◽  
Vehicle Model ◽  
Model Free ◽  
Active Fault Tolerant Control

To solve the problems with the existing active fault-tolerant control system, which does not consider the cooperative control of the drive system and steering system or accurately relies on the vehicle model when one or more motors fail, a multi-input and multi-output model-free adaptive active fault-tolerant control method for four-wheel independently driven electric vehicles is proposed. The method, which only uses the input/output data of the vehicle in the control system design, is based on a new dynamic linearization technique with a pseudo-partial derivative, aimed at solving the complex and nonlinear issues of the vehicle model. The desired control objectives can be achieved by the coordinated adaptive fault-tolerant control of the drive and steering systems under different failure conditions of the drive system. The error convergence and input-output boundedness of the control system are proven by means of stability analysis. Finally, simulations and further experiments are carried out to validate the effectiveness and real-time response of the fault-tolerant system in different driving scenarios. The results demonstrate that our proposed approach can maintain the longitudinal speed error (within 3%) and lateral stability, thereby improving the safety of the vehicles.

scholarly journals Active versus passive fault-tolerant control of a redundant multirotor UAV

2019 ◽  
Vol 124 (1273) ◽  
pp. 385-408
Author(s):  
M. Saied ◽  
B. Lussier ◽  
I. Fantoni ◽  
H. Shraim ◽  
C. Francis
Keyword(s):  
Fault Tolerance ◽  
Fault Detection ◽  
Active Fault ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fault Tolerant Control ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Redundancy Management ◽  
Model Free

ABSTRACTThis paper considers actuator redundancy management for a redundant multirotor Unmanned Aerial Vehicle (UAV) under actuators failures. Different approaches are proposed: using robust control (passive fault tolerance), and reconfigurable control (active fault tolerance). The robust controller is designed using high-order super-twisting sliding mode techniques, and handles the failures without requiring information from a Fault Detection scheme. The Active Fault-Tolerant Control (AFTC) is achieved through redistributing the control signals among the healthy actuators using reconfigurable multiplexing and pseudo-inverse control allocation. The Fault Detection and Isolation problem is also considered by proposing model-based and model-free modules. The proposed techniques are all implemented on a coaxial octorotor UAV. Different experiments with different scenarios were conducted for the validation of the proposed strategies. Finally, advantages, disadvantages, application considerations and limitations of each method are examined through quantitative and qualitative studies.

scholarly journals Model-free reconfiguration mechanism for fault tolerance

2012 ◽  
Vol 22 (1) ◽  
pp. 125-137 ◽  
Author(s):  
Tushar Jain ◽  
Joseph Yamé ◽  
Dominique Sauter
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Vantage Point ◽  
Point Of View ◽  
Mathematical Framework ◽  
Tolerance Mechanism ◽  
Fault Accommodation ◽  
Model Free

Model-free reconfiguration mechanism for fault toleranceThe problem of fault tolerant control is studied from the behavioral point of view. In this mathematical framework, the concept of interconnection among the variables describing the system is a key point. The problem is that the behavior we intend to control is not known. Therefore, we are interested in designing a fault accommodation scheme for an unknown behavior through an appropriate behavioral interconnection. Here we deal simply with the trajectories that are generated by the system in real time. These trajectories determine the behavior of a system in various (faulty/healthy) modes. Based on the desired interconnected behavior, only the trajectories that obey certain laws are selected. These laws, representing the desired behavior, can indeed be achieved by a regular interconnection. Thus, when the trajectories do not belong to a certain desired behavior, it is considered to be due to the occurrence of a fault in the system. The vantage point is that the fault tolerant control problem now becomes completely a model-free scheme. Moreover, no explicit fault diagnosis module is required in our approach. The proposed fault tolerance mechanism is illustrated on an aircraft during the landing phase.

scholarly journals Fault-Tolerant Control for ROVs Using Control Reallocation and Power Isolation

2018 ◽  
Author(s):  
Romano Capocci ◽  
Edin Omerdic ◽  
Gerard Dooly ◽  
Daniel Toal
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Virtual Instrument ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Fault Detection And Isolation ◽  
Control Allocation ◽  
Vertical Layer ◽  
Fault Accommodation ◽  
Model Free

This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault detection units (FDUs), associated with each thruster, to monitor their state. The robust, reliable and adaptive FDUs use a model-free pattern recognition neural network (PRNN) to detect internal and external faulty states of the thrusters in real time. The FA subsystem combines information provided by the FDI subsystem with predefined, user-configurable actions to accommodate partial and total faults and to perform an appropriate control reallocation. Software-level actions include penalisation of faulty thrusters in solution of control allocation problem and reallocation of control energy among the operable thrusters. Hardware-level actions include power isolation of faulty thrusters (total faults only) such that the entire ROV power system is not compromised. The proposed FTC system is implemented as a LabVIEW virtual instrument (VI) and evaluated in virtual (simulated) and real-world environments. The proposed FTC module can be used for open frame ROVs with up to 12 thrusters: eight horizontal thrusters configured in two horizontal layers of four thrusters each, and four vertical thrusters configured in one vertical layer. Results from both environments show that the ROV control system, enhanced with the FDI and FA subsystems, is capable of maintaining full 6 DOF control of ROV in the presence of up to 6 simultaneous total faults in the thrusters. With the FDI and FA subsystems in place the control energy distribution of the healthy thrusters is optimised so that the ROV can still operate in difficult conditions under fault scenarios.

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